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We present a novel spatio-temporal reuse framework for time-resolved light transport, enabling efficient Monte Carlo rendering of time-of-flight (ToF) phenomena such as time-gated imaging and transient light capture. Existing ToF rendering methods are computationally expensive, scale poorly to complex dynamic scenes, and are therefore unsuitable for applications with strict latency constraints. To address this limitation, we draw inspiration from \emph{ReSTIR}, a reuse-based technique for steady-state real-time rendering, and adapt its core principles to interactive-rate ToF simulation. However, naively applying existing ReSTIR methods to ToF rendering leads to severe inefficiency, as reused paths frequently violate optical path-length constraints and thus contribute little or no signal. We overcome this challenge by introducing a path reuse formulation that explicitly enforces physically valid optical path lengths. The key idea is \emph{path-length-aware shift mapping}, a geometric transformation based on Newton’s method that adjusts reused light paths to satisfy temporal gating constraints, inspired by specular manifold exploration in steady-state caustics rendering. The resulting framework substantially improves the efficiency of ToF rendering across a wide range of scenarios, including complex scenes with glossy or specular materials and dynamic motion. Our method supports both time-gated and transient rendering at interactive frame rates, enabling simulation under practical latency constraints. We demonstrate the effectiveness of our approach through two downstream applications, including shape reconstruction and navigation. 

We present EventSVGF, an event camera rendering framework based on spatiotemporal variance-guided filtering (SVGF)~\cite{Schied:2017:Spatiotemporal}, designed to achieve high temporal accuracy especially in high-frequency regions, which is critical for faithful event simulation. Unlike conventional rendering, event cameras measure temporal changes in brightness (log-intensity), requiring accurate estimation of per-pixel, frame-to-frame differences. However, naively computing temporal differences from primal-domain RGB images leads to severe noise, as existing denoising methods are designed for primal signals rather than their differences. Our key contribution is a method that directly denoises the pixel-wise temporal difference signal using correlated sampling, formulated as a difference-aware extension of the SVGF pipeline, termed EventSVGF. EventSVGF incorporates a novel edge-stopping function, an adapted temporal accumulation scheme, and an albedo demodulation strategy, all tailored for accurate event camera simulation. Our method achieves stable results at low sampling rates (2 spp), whereas existing approaches typically require significantly higher sampling budgets (32--512 spp). We demonstrate EventSVGF on dynamic scenes, showing improved accuracy and high-frequency temporal stability in event simulation compared to prior works. 

Monte Carlo time-gated rendering requires sampling light paths that not only connect a sensor to an emitter, but which also have a total travel time that falls within a narrow interval, a constraint that is difficult to importance sample. We show that this problem has an underlying geometric structure: in the joint space of position and accumulated travel time, the points yielding a time-valid connection to a given query point form a light-cone shell. Prior methods sample this shell indirectly. Steady-state algorithms sample the full space and reject points outside it, giving high variance under tight gates. Ellipsoidal path connections target a single cone surface by intersecting an ellipsoid with scene geometry, coupling cost to scene complexity. Our key observation is that shell membership is cheap to test, needing only accumulated travel time and a Euclidean distance. We therefore store the vertices of traced light subpaths in a 4D spatiotemporal hierarchy and recast time-gated connection as a range query, using pruning and importance sampling over the shell to select time-valid vertices without intersecting scene geometry. This decouples the cost of time gating from scene complexity. Within a bidirectional path tracing framework, our method significantly reduces variance over existing approaches on scenes with up to 2.4M triangles. 

Event cameras capture changes in log intensity asynchronously, enabling high temporal resolution and dynamic range for dynamic scenes. However, because they respond only to intensity changes, they are fundamentally insensitive to static or slowly varying content, leading existing event-to-video methods to rely on camera motion or hallucinate missing structure. Prior efforts to recover static information either exploit Poisson noise events—requiring long integration times—or employ mechanical shutters to convert event cameras into intensity sensors, sacrificing temporal resolution. We introduce \emph{ShutterEvents}, a lightweight sensing framework that enables simultaneous recovery of static and dynamic scene content using a low-cost (\$7) programmable LCD shutter. By imposing controlled temporal modulation on incoming light, static intensities are transformed into predictable brightness variations that generate informative events even under a stationary camera. We show that the induced mapping between event statistics and scene intensity is injective, enabling a learning-free reconstruction of static structure directly from event measurements. To separate shutter-induced responses from true scene dynamics, we propose an event decomposition algorithm that leverages the global temporal synchronization introduced by the modulation signal. Experiments on both simulated and real data demonstrate that \emph{ShutterEvents} recovers meaningful static structure while preserving dynamic fidelity, without long integration times and with minimal hardware overhead. 

We present a high-speed underwater optical backscatter communication technique based on acousto-optic light steering. Our approach enables underwater assets to transmit data at rates potentially reaching hundreds of Mbps, vastly outperforming current state-of-the-art optical and underwater backscatter systems, which typically operate at only a few kbps. In our system, a base station illuminates the backscatter device with a pulsed laser and captures the retroreflected signal using an ultrafast photodetector. The backscatter device comprises a retroreflector and a 2 MHz ultrasound transducer. The transducer generates pressure waves that dynamically modulate the refractive index of the surrounding medium, steering the light either toward the photodetector (encodingbit1) or away from it (encodingbit0). Using a 3-bit redundancy scheme, our prototype achieves a communication rate of approximately 0.66 Mbps with an energy consumption of ≤ 1 μJ/bit, representing a 60× improvement over prior techniques. We validate its performance through extensive laboratory experiments in which remote underwater assets wirelessly transmit multimedia data to the base station under various environmental conditions. 

Event cameras, which feature pixels that independently respond to changes in brightness, are becoming increasingly popular in high- speed applications due to their lower latency, reduced bandwidth requirements, and enhanced dynamic range compared to traditional frame- based cameras. Numerous imaging and vision techniques have leveraged event cameras for high- speed scene understanding by capturing high- framerate, high- dynamic range videos, primarily utilizing the temporal advantages inherent to event cameras. Additionally, imaging and vision techniques have utilized the light field—a complementary dimension to temporal information—for enhanced scene understanding.In this work, we propose "Event Fields", a new approach that utilizes innovative optical designs for event cameras to capture light fields at high speed. We develop the underlying mathematical framework for Event Fields and introduce two foundational frameworks to capture them practically: spatial multiplexing to capture temporal derivatives and temporal multiplexing to capture angular derivatives. To realize these, we design two complementary optical setups— one using a kaleidoscope for spatial multiplexing and another using a galvanometer for temporal multiplexing. We evaluate the performance of both designs using a custom-built simulator and real hardware prototypes, showcasing their distinct benefits. Our event fields unlock the full advantages of typical light fields—like post- capture refocusing and depth estimation—now supercharged for high- speed and high- dynamic range scenes. This novel light- sensing paradigm opens doors to new applications in photography, robotics, and AR/VR, and presents fresh challenges in rendering and machine learning. 

We present a wave‐optics‐based BSDF for simulating the corona effect observed when viewing strong light sources through materials such as certain fabrics or glass surfaces with condensation. These visual phenomena arise from the interference of diffraction patterns caused by correlated, disordered arrangements of droplets or pores. Our method leverages the pair correlation function (PCF) to decouple the spatial relationships between scatterers from the diffraction behavior of individual scatterers. This two‐level decomposition allows us to derive a physically based BSDF that provides explicit control over both scatterer shape and spatial correlation. We also introduce a practical importance sampling strategy for integrating our BSDF within a Monte Carlo renderer. Our simulation results and real‐world comparisons demonstrate that the method can reliably reproduce the characteristics of the corona effects in various real‐world diffractive materials. 

Optical heterodyne detection (OHD) employs coherent light and optical interference techniques (Fig. 1-(A)) to extract physical parameters, such as velocity or distance, which are encoded in the frequency modulation of the light. With its superior signal-to-noise ratio compared to incoherent detection methods, such as time-of-flight lidar, OHD has become integral to applications requiring high sensitivity, including autonomous navigation, atmospheric sensing, and biomedical velocimetry. However, current simulation tools for OHD focus narrowly on specific applications, relying on domain-specific settings like restricted reflection functions, scene configurations, or single-bounce assumptions, which limit their applicability. In this work, we introduce a flexible and general framework for spectral-domain simulation of OHD. We demonstrate that classical radiometry-based path integral formulation can be adapted and extended to simulate the OHD measurements in the spectral domain. This enables us to leverage the rich modeling and sampling capabilities of existing Monte Carlo path tracing techniques. Our formulation shares structural similarities with transient rendering but operates in the spectral domain and accounts for the Doppler effect (Fig. 1-(B)). While simulators for the Doppler effect in incoherent (intensity) detection methods exist, they are largely not suitable to simulate OHD. We use a microsurface interpretation to show that these two Doppler imaging techniques capture different physical quantities and thus need different simulation frameworks. We validate the correctness and predictive power of our simulation framework by qualitatively comparing the simulations with real-world captured data for three different OHD applications—FMCW lidar, blood flow velocimetry, and wind Doppler lidar (Fig. 1-(C)). 

We introduce a structured light system that enables full-frame 3D scanning at speeds of \SI{1000}{\fps}, four times faster than the previous fastest systems. Our key innovation is the use of a custom acousto-optic light scanning device capable of projecting two million light planes per second. Coupling this device with an event camera allows our system to overcome the key bottleneck preventing previous structured light systems based on event cameras from achieving higher scanning speeds---the limited rate of illumination steering. Unlike these previous systems, ours uses the event camera's full-frame bandwidth, shifting the speed bottleneck from the illumination side to the imaging side. To mitigate this new bottleneck and further increase scanning speed, we introduce adaptive scanning strategies that leverage the event camera's asynchronous operation by selectively illuminating regions of interest, thereby achieving effective scanning speeds an order of magnitude beyond the camera's theoretical limit. 

Transient absorption spectroscopy (TAS) is a field of study that investigates the dynamic process of chemical compounds. Thanks to the recent emergence of ultrafast pulsed lasers, TAS now extends its reach to studying photochemical reactions occurring within few femtosecond to nanosecond timescales. With ultrafast TAS, changes in sample absorbance or transmittance over time following excitation by pulsed light can be measured at a high temporal resolution -tens of femtoseconds. An application of ultrafast TAS is lifetime measurement for fluorescence decay. However, due to various noise sources (sensor noise, shot noise, unintended photochemical reactions, etc.) during measurement, obtaining a reliable lifetime value often necessitates extensive repetition resulting in experiments lasting several hours. In this paper, we introduce an effective time sampling strategy tailored for lifetime measurement from noisy transient signals. We start with a well-established non-linear curve fitting algorithm and demonstrate that sampling time shifts that maximize the signal derivative (t=τ) will minimize the variance in lifetime estimation. Additionally, we reduce the number of parameters by normalization to ensures the correctness of our algorithm. We demonstrate using simulation that our proposed method outperforms conventional time sampling or normalization methods across various conditions. Especially, we found that proposed method gives same error with 5.5 x less samples compared to the common TAS measurement strategy that uses exponential time sampling with full parameter curve-fitting. Moreover, through real-world TAS measurements, we show that our technique results in 2 - 8 x less standard deviation compared to baseline methods. We expect that our algorithm will be valuable not only for researchers who use TAS but also for researchers across various fields who use time-gated transient cameras for lifetime analysis.